Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An ontology in philosophy is a description of what exists; an ontology in information science is the formal specification of a domain for information organization. A top-level ontology is an ontology of high-level abstractions that is meant to provide a standard for other ontologies to make them interoperable. Top-level ontologies commit to certain philosophical assumptions in the worlds they model, such as naive scientific realism, or the choice to model only those worlds which “actually” exist. This paper uses a comparison of Basic Formal Ontology (BFO) and Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) to investigate whether naive scientific realism is useful as a philosophical stance in ontological modelling. It investigates the specific philosophical and technical features of DOLCE which make it unique. BFO and DOLCE are compared, as well as a miniature literature review of comparisons between other top-level ontologies. Then it describes the history of DOLCE and BFO’s development in the context of the collaboration between Barry Smith and Nicola Guarino. Finally, it examines the application of DOLCE in various domains, such as “sweetening” WordNet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it